Advancing medical diagnostics through precision engineering and state-of-the-art neural architectures.
Our research team are expert in creating & testing AI models that can classify medical images.
We use those AI models to create medical devices that redefine patient care standards.
class FatCubeEngine:
def analyze(scan):
# Initializing DalNet...
result = model.process(scan)
return result.confidence
A comprehensive suite of specialized neural networks designed for high-precision medical image analysis.
157M parameters (0.5869 GB)
Multi-Scale Feature Extraction with Spatial and Channel Attention mechanisms for granular detail capture.
Learn More422M parameters (1.5722 GB)
Dual-Branch Multi-Scale Convolution with Progressive Depth for complex pattern recognition.
Learn More564M parameters (2.1045 GB)
Rotation-Invariant Quad-Model Architecture (0°, 90°, 180°, 270°) for consistent diagnostic output.
Learn More221M parameters (0.8248 GB)
Core Processing Block repeated 42 times with 5 parallel branches for high-throughput processing.
Learn More205M parameters (0.7667 GB)
Five Clinically-Specialised Expert Branches with Progressive Multi-Level Fusion for targeted analysis.
Learn More288M parameters (1.0759 GB)
Dual-Backbone Feature Extraction (CNN + Global Vision Transformer) for global and local context.
Learn More24M to 517M parameters (0.9 GB to 1.9266 GB)
Comparing every patch of the image to every other patch, at once. Not just the adjacent patch.
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